Embedding large language model (LLM) coordinators in production electronic systems, connected vehicles, multi-robot fabrics, IoT control loops, telecommunications orchestration, demands a pre-delivery filter stage that preserves ethical guarantees under adversarial influence at deployment scale. We present a constitutional governance layer that filters compiled influence policies before they reach a heterogeneous population of grounded LLM agents whose hybrid decision model combines a game-theoretic base probability with an LLM-evaluated narrative shift attenuated by per-agent resistance. Four experiments on a Barabási–Albert scale-free network of 30 agents powered by Llama-3.3-70B-Instruct show that the filter holds an Ethical Cooperation Score (ECS) of 0.176 (multi-seed mean 0.163, 95% confidence interval (CI) 0.150,0.174) against an unconstrained baseline of ECS=0, enforced by a hard integrity gate (1.000 vs. 0.000). We surface an autonomy paradox in which unconstrained agents resist manipulation more forcefully (0.856 vs. 0.728) yet collapse to ECS=0, establishing that system-level integrity cannot be delegated to agent-level defence. The advantage is monotonic in resistance (+0.174 to +0.183), seed-stable (Cliff’s δ=1.0, complete separation), topology- and backbone-invariant across five contemporary LLMs, robust to alternative ECS formulations, and reproduces at N = 100. Against constitutional artificial intelligence (CAI) critique-revise and LlamaGuard-style safety-classifier baselines, the framework matches the integrity floor and adds a measurable margin on the secondary risk surface (burst timing, composite manipulation risk). The filter runs at 0.78 μs/call (≈1.3×106 decisions/s/core), supporting always-on deployment as a stateless, model-agnostic component of LLM agent pipelines in adversarially contested electronic systems.
Curtò et al. (2026) studied this question.